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COPULAENDOG: Stata module to correct endogenous regressors in a linear model when no instrument is available

Author

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  • Girish Mallapragada

    (Kelley School of Business, Indiana University)

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Abstract

copulaendog corrects endogenous regressors in a linear model when no instrument is available. The dependence between the regressor and the structural error is modelled with a Gaussian copula, and the resulting control function is added to the regression. Five cross-sectional estimators sit behind one interface: Park and Gupta (2012), the two-stage copula generated regressor approach of Yang, Qian and Xie (2025), the between-regressor correlation variant of Haschka (2025), the nonlinear-transformation approach of Breitung, Mayer and Wied (2024), and the adjusted estimator of Liengaard et al. (2025). Six marginal CDF estimators are available for the copula transformation, inference is by pairs bootstrap, and a validity option reports the identification requirements the literature states — non-normality of the endogenous regressor on both the Yang and the Becker criteria, the uncorrelatedness assumption where it applies, and the ICON standard-error inflation of Qian, Koschmann and Xie (2025).

Suggested Citation

  • Girish Mallapragada, 2026. "COPULAENDOG: Stata module to correct endogenous regressors in a linear model when no instrument is available," Statistical Software Components S459859, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:s459859
    Note: This module should be installed from within Stata by typing "ssc install copulaendog". The module is made available under terms of the MIT license (https://opensource.org/licenses/MIT).
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